
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
							import tensorflow as tf


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)

@dataclass
class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			[
			    """no_inference""",
			    """no_cuda""",
			    """no_tpu""",
			    """no_speed""",
			    """no_memory""",
			    """no_env_print""",
			    """no_multi_process""",
			]

			def __init__(							self			:							Optional[Any]    ,						**snake_case_			:							Union[str, Any]   ):
							for deprecated_arg in self.deprecated_args:
											if deprecated_arg in kwargs:
															UpperCamelCase_:				int						=    deprecated_arg[3:]
															UpperCamelCase_:				Optional[int]						=    not kwargs.pop(snake_case_   )
															logger.warning(
															    f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
															    f''' {positive_arg}={kwargs[positive_arg]}'''   )
							UpperCamelCase_:				Tuple						=    kwargs.pop("""tpu_name"""    ,						self.tpu_name   )
							UpperCamelCase_:				Optional[int]						=    kwargs.pop("""device_idx"""    ,						self.device_idx   )
							UpperCamelCase_:				List[Any]						=    kwargs.pop("""eager_mode"""    ,						self.eager_mode   )
							UpperCamelCase_:				List[Any]						=    kwargs.pop("""use_xla"""    ,						self.use_xla   )
							super().__init__(**snake_case_   )

			__UpperCamelCase   :    str          =			field(
			    default=_A  ,							metadata={"""help""": """Name of TPU"""}  ,							)
			__UpperCamelCase   :    int          =			field(
			    default=0  ,							metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}  ,							)
			__UpperCamelCase   :    bool          =			field(default=_A  ,							metadata={"""help""": """Benchmark models in eager model."""}	)
			__UpperCamelCase   :    bool          =			field(
			    default=_A  ,							metadata={
			        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
			    }  ,							)

			@cached_property
			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							requires_backends(self    ,						["""tf"""]   )
							UpperCamelCase_:				List[str]						=    None
							if self.tpu:
											try:
															if self.tpu_name:
																			UpperCamelCase_:				Dict						=    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name   )
															else:
																			UpperCamelCase_:				str						=    tf.distribute.cluster_resolver.TPUClusterResolver()
											except ValueError:
															UpperCamelCase_:				Dict						=    None
							return tpu

			@cached_property
			def    lowerCAmelCase__						(							self			:							List[str]   ):
							requires_backends(self    ,						["""tf"""]   )
							if self.is_tpu:
											tf.config.experimental_connect_to_cluster(self._setup_tpu   )
											tf.tpu.experimental.initialize_tpu_system(self._setup_tpu   )

											UpperCamelCase_:				List[str]						=    tf.distribute.TPUStrategy(self._setup_tpu   )
							else:
											# currently no multi gpu is allowed
											if self.is_gpu:
															# TODO: Currently only single GPU is supported
															tf.config.set_visible_devices(self.gpu_list[self.device_idx]    ,						"""GPU"""   )
															UpperCamelCase_:				Optional[Any]						=    tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''   )
											else:
															tf.config.set_visible_devices([]    ,						"""GPU"""   )  # disable GPU
															UpperCamelCase_:				int						=    tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''   )

							return strategy

			@property
			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							requires_backends(self    ,						["""tf"""]   )
							return self._setup_tpu is not None

			@property
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							requires_backends(self    ,						["""tf"""]   )
							return self._setup_strategy

			@property
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							requires_backends(self    ,						["""tf"""]   )
							return tf.config.list_physical_devices("""GPU"""   )

			@property
			def    lowerCAmelCase__						(							self			:							Dict   ):
							requires_backends(self    ,						["""tf"""]   )
							if self.cuda:
											return len(self.gpu_list   )
							return 0




			@property
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							return self.n_gpu > 0








def 							A__       (    lowerCamelCase = 4_00_00_00    )	->						int:
				UpperCamelCase_:				Dict						=    []
				UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    0, 1
				while b <= n:
								if b % 2 == 0:
												even_fibs.append(lowerCamelCase    )
								UpperCamelCase_, UpperCamelCase_:				int						=    b, a + b
				return sum(lowerCamelCase    )


if __name__ == "__main__":
							print(F"""{solution() = }""")






